Evaluation of breeding cost in the French maritime pine breeding program and perspectives for alternative strategies using molecular markers
Bibliographic record
Abstract
The economic efficiency of conventional breeding strategies for forest trees based on biparental crosses is compared with that of alternative strategies based on pedigree reconstruction using molecular markers. Analyses of economic efficiency is based on comparisons of breeding scenarios corresponding to the same total investment. The first step is the description and cost evaluation of each basic operation, from crossing to genetic selection and clonal archive establishment. Breeding scenarios are then compared by stochastic sampling with a parametric genetic model (POPSIM), the comparison criteria in this case being genetic gain in the seed orchard for a given level of genetic diversity. Additionally, the economic gain resulting from the use of improved material is estimated for different levels of breeding investment. Our analysis shows that genotyping costs account for a much smaller proportion of total investment than phenotyping costs. We also show that, in comparisons of breeding scenarios corresponding to the same total investment, the three main breeding strategies (biparental crosses, polymix crosses, and open pollination) achieve similar genetic gains provided that sufficiently large numbers of parents are considered. These results open up promising perspectives for the wider integration of molecular markers into forest tree breeding strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".